# Online Learning of Human Constraints from Feedback in Shared Autonomy

arXiv: https://arxiv.org/abs/2403.02974 · HTML: https://mlsanity.com/paper/2403.02974/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 13 references that are in the map.
- First posted on arXiv 5 Mar 2024.

## Builds on (most cited first, 5 of 5)

- [Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability](https://mlsanity.com/paper/1703.06182/) (2017)
- [Efficient Model Learning for Human-Robot Collaborative Tasks](https://mlsanity.com/paper/1405.6341/) (2014)
- [Learning to Collaborate from Simulation for Robot-Assisted Dressing](https://mlsanity.com/paper/1909.06682/) (2019)
- [An Efficient, Generalized Bellman Update For Cooperative Inverse Reinforcement Learning](https://mlsanity.com/paper/1806.03820/) (2018)
- [Using exoskeletons to assist medical staff during prone positioning of mechanically ventilated COVID-19 patients: a pilot study](https://mlsanity.com/paper/2102.08760/) (2021)

## Abstract (arXiv)

Real-time collaboration with humans poses challenges due to the different behavior patterns of humans resulting from diverse physical constraints. Existing works typically focus on learning safety constraints for collaboration, or how to divide and distribute the subtasks between the participating agents to carry out the main task. In contrast, we propose to learn a human constraints model that, in addition, considers the diverse behaviors of different human operators. We consider a type of collaboration in a shared-autonomy fashion, where both a human operator and an assistive robot act simultaneously in the same task space that affects each other's actions. The task of the assistive agent is to augment the skill of humans to perform a shared task by supporting humans as much as possible, both in terms of reducing the workload and minimizing the discomfort for the human operator. Therefore, we propose an augmentative assistant agent capable of learning and adapting to human physical constraints, aligning its actions with the ergonomic preferences and limitations of the human operator.